{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "from baselines.common import plot_util as pu"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you want to average results for multiple seeds, LOG_DIRS must contain subfolders in the following format: ```<name_exp0>-0```, ```<name_exp0>-1```, ```<name_exp1>-0```, ```<name_exp1>-1```. Where names correspond to experiments you want to compare separated with random seeds by dash."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "LOG_DIRS = 'logs/reacher/'\n",
    "# Uncomment below to see the effect of the timit limits flag\n",
    "# LOG_DIRS = 'time_limit_logs/reacher'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/kostrikov/GitHub/baselines/baselines/bench/monitor.py:163: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  df.headers = headers # HACK to preserve backwards compatibility\n"
     ]
    }
   ],
   "source": [
    "results = pu.load_results(LOG_DIRS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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YFt/jqiq7jVAIvva10f/mT2S84aSp5KeAPd17eHDngzy25zGWNy6nOljNT3f8lOcPP09f2v6PrQxUMjMyk8sXXs67VryLRfWLiGfi/GLXL5hXPY9zZ55LdaiaqkAVQV/whFNwi4YfND9dHT4M991nG/YZM+AP/9B+Sq+qsv/xAwH7nzidHmpQP/95++n/4ottg9fVZbfz9NP2E/yePfbnYxUbmmNVV8Mb3mDDrKNjaPncubam7dth61bbOEUidrgmlbKNXmWlramtDVasGGqUli2zDeOuXXb7sZhtcIq9Bsexz581a6hBT6Xs12IDmU7b/TuObURjMbt89mz7WKFgH29utl99Pvse7N1r99HUNNRAFnsOZ5wBS5bYbVZW2l5NPD7U24hE7PtXbEiLoVEo2OcXG05j7OvLZu12qqpsfaGQ/X14PPa5uZzdTk2NfT8bG+HZZ23Ytbfbuisq7HsUjdrX4Pfb3lxlpe1V9fTY/dXV2WB48UX7NzJzpt1uoQC//vVQz7S+3r6maHTob6f4HjqOrdHns6+1rc3WUAyqhga733374Lzz7L9Dh4YCPxi0v9N9++z7W1lpH1u50v5uh38oKPY66+rs85JJ+/uOROzvubvbvrZAwD6vOMyYz9t1i+/JkSOv+ZOdcuo5lal8Ic8nf/VJ7tp81+B02gp/xeCwXF2ojlnRWSysXciShiWc13wei+oWsbhh8eDwmBvE47Bliw2Xc86B3/s920B1d9sGo9j4/PKX8OCDtmF697tt49HWZnspTz119FBO8RNoNGobsMZG22j39g4dfygOkR37Cd4Y+9zaWjvMMneufS4MBUhzs102d65tIHp67LGDI0dsY/D7v28by2DQ1jlnjm0oDhywjWahAAsW2CG33buHhpV6emyDunCh7bXFYra3sHHjUGNVKNjGrLbWbsvnsw3T2Wfb19PTY0PV67WN5Isv2te/eLENjL17be0LFtgg3LnTNryrVtllYIPh6aftdlassPvs6rKvI5ezNTU0DL1nuZz9PVZVHT3seOSIfcxxbIPa32/rCoft+oHAUGBmMvZDQjw+NLTZ2Wnfz/377RBhc/PQPvN5W3drq91eS8vQUNpYZLP2/RuvZHIoTItBm8nY11Io2MeLkxmKw3CnCw3rneIy+Qzf3vJtvr3l2/x6369ZULOAlmgL5zady1VnXUVHooPNhzbzxvlv5Lzm82iubMbr8Za67HHJZOwnwULBBkBtrQ2Pri7bmNXVDQ2L/fjH9lOuzwfr1tnjI+eeaxvTn/wEnnnGPm8sip+sh//JB4OwerUNkqqqoU+axUa4eIA6FLK1JhK2ITv3XLt+a6tt6IoHu+fOtesuWGBDYs4c+zq7u+3jzzxj97twoW0cFy2yjXhbGzz+uG20Vq+2rzWTsdsLh+1zOzvtth3H7nuskklbo9dr36uZM+33ItNJ4XQK29uzl2v/61qeP/w8fo+fdS3ruP0Nt7OiaQUNFQ2nzLEexxk6GP+zn9nw6euzPZzf/MYGTEfH0LCO328b76Lip8hib6ax0X6iP/Y4gtc7dOC8ONziOEO9geIwUjxue09NTTZ4WluH1pk1yz62YoX9PpWyPY1s1oZBcbjL6x0aSqqthWuvtZ/Ei8dv8nk7zNfYaD/Bh0+i87pnj93/WWcpPOT0o3A6xfSn+/mnp/+JuzbfxaHYIfweP29a8CauWHgF6+asY+2stWV7bkZfH9x/v53G+thjR88yCgTscEo6ffRzIhHbq5g5c2iMO522PQSPx35fnL5bU2OPbVRV2U//3d02SHbtssFTnDlWWWnH22trbW/L57OBEo/bnksqZbd50UX2Odu32/309dnjH+ec89opyI5jexnV1bZnk8vZeoqz4iZzCrSIG2hCxClk44GNXPHdK+hMdjInOoc1s9awdvZabl51MyuaVpS6vNfo6LBDaR4P/PCHdppwcdbWjBlDU2GL53IsX257NV1d9vHa2qF1Zs2yvYx02vZ4gkF7AP/IEbvNw4ft8ZZ162xwBQJ2yC0YtEHz0kt2KG7WLPuv2NNYt+7Er2PGjBOvY4zdP9hhPBGZXgqnEuhMdPK9332PO564g1whxzuXv5N3nvNOljUuo6GiYfCqBKXkOPZkvQcftENcu3fDww8PhZHPZ4+7LF5sG/tIxB7kTiRs4BQP5p5/vu35tLfbdRsb7XrB4PhrWr586Pvzzpuc1yki5UnhNM3+ddO/8hcP/wWpXIr6cD3XL7+ez7zxMzRGGktSj+PYYzAPP2x7RXv22FDZv3/oBD6wPZP5823YgO1NVFXZkFi50vaIimfcF7dbPONdRGS81HRMo+//7vvc8uAtzInOYcPCDbxl0Vu4aO5FNFQ0nPjJk6B47kJ3tx22SqXg7W+3ExXADp01NtpeT1OTPUYzf75dHonY559zztBxoCVLjn/g3xgFk4icPDUf0+TBHQ/yx/f9MbOrZvPx132c96x8D5HAKFdnnCT79sG//Zu9vMvzzw9Nu/b7bdgEAvbcoHnzbOg0Ndkg8vvtMNqCBUMz5zweO5GhTOdniMhpROE0De57+T7e9oO30VDRwPvPez/vO/99U3bFhZ4e+Ku/sseLfD57EmWhYGeaVVXZnlBtrT2vJpu1Vzh4y1vsrLXmZjslW0Sk1BROU+y3R37LDT++gaZIEx9e82E+cuFHpiSYHMeerPrOd9oTSBsb7bLzzrOXulmxwvZ8zjrLhlJ9vb1cz7p1dtabiEg5UThNoa5kF1d//2r8Hj/vP+/9fPSij05qMPX22isrPPcc/Nd/2anW4TD8wR/AO94xNBT3+tfb6dbHWrVq0koREZlUCqcpki/kueFHN9Da18pNq27ittffNnjTtMmwdaudzLB9u/25qQne+Ea44AK48UY7bVtE5FSlcJoitz9+O4+88ghXLLyCv3/j309KMOXz9tI/d9xhe0qBAFxzjZ3MsGgRXHqpHaILnJ73phMRF1E4TYGfvPQT/uE3/8A5jefw1xf99aRMFf/ud+FDH7KX3PH57IVDr77a3qFy1ixdTkdETi8Kp0n2csfLvPsn76a5spn3n/d+fm/e701oe/39cMstNpyam2HtWjtk99a32vOQTuYCoyIi5U7hNIkcx+GDP/sgBafAu1a8i5tW3TTmm/qNZPt2e2+fV16xlwq64QZ7PKm2VkN3InJ6UzhNosf2PMb/vvq/vPmMN/NXF/3VhG7619MDV11lL4D69rfD9dfD+vXju4+PiMipSuE0ie588k4q/ZW8e8W7T/paeR0d8Kd/Cv/7v/YK3e95D3z2s0ffxVNE5HSncJokT+9/mkdeeYQ3zn8jV5515Ulto7/fTgffts2eRPuWt8DttyuYRMR9FE6T5O8e/zsi/gjXLL6GuvD4rwG0dSu87W2wc6cdzrv1Vnt/I91LSETcSBOQJ8Ezrc/wP3v+h7Wz1/KO5e8Y9/P/3/+zlxfav99OD7/tNtiwQcEkIu6lntMk+Mff/CNhX5jrllzHzMqZ43ruvffC3/7t0HlLt9xivxcRcTOF0wRt79jO/dvv56I5F3Hd8uvG9dxt2+Cmm+xJtJ/4hL0eXmhqLlYuInJK0bDeBH3zuW/iMR7efOabx3UliK1b4ZJL7PfvfKeCSURkOPWcJqDgFPje1u9xRu0ZvGvFu8b8vJ07bTBlMnaq+F//tYJJRGQ49Zwm4Ml9T3Kw/yDnNJ7D3Oq5Y3pOImFn5SWT8L73wac+BTNmTG2dIiKnGoXTBHx/6/fxe/xcvfjqMV2myHHsMaatW+3kh09+Ehomfk1YEZHTjsJpAh7a+RALahbwhvlvGNP6X/0qfO979p5LH/+4vUaeiIi8lsLpJLX2tfJq76vMr5lPS/TEJyT9+tfw0Y/C/PnwgQ/Y85pERGRkCqeT9MSrTwCwpGHJCYf0jhyBa6+Fykp7ZfG3vx2MmY4qRUROTVMWTsaY/88Y87Ix5rfGmJ8YY2qGPfY3xphdxpjtxpgNU1XDVHp87+MEvUGuXXLtCdf9i7+Ari74gz+wF3WtrJyGAkVETmFT2XP6JbDccZwVwA7gbwCMMUuB64FlwOXAvxhjvFNYx5R4fM/jtERbWFC7YNT1fvUr+P737f2YbrlFF3EVERmLKQsnx3EecRwnN/DjM0DxwMw1wL2O46Qdx9kD7ALWTlUdU6Ej0cHu7t3MqZ4z6q0xHMeew1RVZU+yXbVqGosUETmFTdcxp/cBPx/4fjawf9hjrQPLXsMYc7MxZpMxZlN7e/sUlzh2zx16DoAFNQsI+oLHXe+JJ2DjRhtKf/RH4D3l+ociIqUxoStEGGMeBUa60ultjuPcP7DObUAO+O54t+84zl3AXQCrV692JlDqpCqG0+/N/b1R1/vMZ6CiAj74QV1hXERkPCYUTo7jXDba48aYG4GrgEsdxymGywFgzrDVWgaWnTKePfAsNaEaVs9afdx1Nm+Gxx6DN7zB3v5CRETGbipn610O/DVwteM4iWEPPQBcb4wJGmMWAIuAZ6eqjqmw+eBmmiJNo17o9Y47IBi0F3WtqTnuaiIiMoKpPOb0VaAK+KUx5gVjzL8COI6zDfgB8CLwC+BWx3HyU1jHpOpOdrOvbx8zK2ceN5xeegnuvx9WroQrT+6O7SIirjZlVyV3HOe4t8xzHOezwGenat9T6YXDLwAwr3oeAW9gxHXuuAN8PhtMOtYkIjJ+ukLEOP2u7XcArGga+fpDe/fau9uefba9EoSIiIyfwmmcXmp/iaA3yLqWdSM+/rGP2a+XXgqLFk1jYSIipxGF0zhtbd9KXbhuxJNvX3gBfvADWL7cXkvP7y9BgSIipwGF0zjt7NxJXbiOmtBrp+B94Qs2kC67DM47rwTFiYicJhRO49Cf7udI/Aj14XqqQ9VHPZZIwI9+BGeeaS9VpIu7ioicPIXTOOzo3AHAzMqZr5mp99Of2luvr1qlezWJiEyUwmkctnduBxjxSuT33GN7S297G4RC01yYiMhpRuE0Di+1v4TBcGHLhUct7+mBRx+FhQvh/PNLVJyIyGlE4TQO29q3EQ1GmVs996jlv/gF5HL2nk066VZEZOIUTuOwq2sXNaGa10yG+O//hnAYfv/3dVsMEZHJoHAah1d7X6U6WE00GB1clsvBww/D3Lm25yQiIhOncBqj7mQ3fek+6irqjgqnp56Cvj5YuhTmzSthgSIipxGF0xi90v0KAI0VjUdNI7//fvB47D2bgse/Ka6IiIyDwmmMiuF07GSI+++H2bPhda8rRVUiIqcnhdMY7ezaCcCqWasGl+3aBbt32ynkc+ce75kiIjJeCqcx2tG5g7AvzBm1Zwwu+9nP7Nfzz4do9DhPFBGRcVM4jdHOrp1Uh6qPuuDrT38KdXXwlreUsDARkdOQwmmMXu2x08irAlUA5PPwzDP2pNszzjjBk0VEZFwUTmOQyWc4FDtEbaiWqqANp61b7ZXIFyyA5uYSFygicppROI3Bvt59FJwCDZEGIv4IYM9vAntjQZ+vhMWJiJyGFE5jsLtrNwBNkSaMMQD8+tf2KuQbNpSyMhGR05PCaQyK5zgtbVg6uOzJJ2HWLJgzp1RViYicvhROY7Cjawc+j4+ljTacWlth3z578m1TU4mLExE5DSmcxmB3126qAlXMrJwJwC9/aZcvXWqvRi4iIpNL4TQG+/v2Ew1GB6eRP/IIRCI63iQiMlUUTmNwsP8glYHKwWnkv/mNPb9pyZISFyYicppSOJ1ANp+lPd5ONBilMlDJoUP2mFNLi+56KyIyVRROJ3AodggHh9pQLWFfmKeftsvPOgtCodLWJiJyulI4nUBrXysA9eF6jDE8/bS9Ffvll5e4MBGR05jC6QT29+4HoKXajuH95jf2ckUrVpSyKhGR05vC6QT299lwWt64nGwWnnvOHmtqbCxxYSIipzGF0wns7dmL3+NnfvV8Xn4ZMhkbThUVpa5MROT0pXA6gVd7X6UqWEV1qJoXXrDLFi4sbU0iIqc7hdMJ7O/dT1Wgikggwgsv2CuQ6+RbEZGppXA6gUOxQ1QGKon4I2zebK+ld+aZpa5KROT0pnAaxfATcMO+CrZsgZkzoaGh1JWJiJzeFE6jGDwBN1xL55EwPT12Grku9ioiMrUUTqOkRPFlAAAgAElEQVQonuNUF6rjt1vsWzV3bikrEhFxB4XTKIpXh5hZOZOXX7bL1q4tYUEiIi6hcBpF8QTcpTOWsn27vU3GeeeVuCgRERdQOI1i8ATcmvm8+CLU12syhIjIdFA4jWJ/334qA5XUhevYudOGU01NqasSETn9KZxGcbD/IJFAhEKqio4OG066bJGIyNRTOI3iSOwIEX+Ew3ujgD3HSUREpp7C6Tgcx6Et3kZloJLWPREAli4tcVEiIi6hcDqOvnQf6XyaaDDK3t1BjIGLLy51VSIi7jDl4WSM+UtjjGOMaRj42RhjvmyM2WWM+a0x5vypruFkHOw/CEBNqIaXX/JQUwOzZ5e4KBERl5jScDLGzAHeDOwbtvgKYNHAv5uBr09lDSfrUOwQAHXhOrZv10w9EZHpNNU9py8Cfw04w5ZdA3zbsZ4BaowxzVNcx7gd6rfh1FI1l927bThFoyUuSkTEJaYsnIwx1wAHHMfZcsxDs4H9w35uHVhWVoo9p8b8SlIpe1t2r7fERYmIuIRvIk82xjwKjDTB+jbgb7FDehPZ/s3YoT/mTvMVV/f37cfn8ZFvt7e9nTNnWncvIuJqEwonx3EuG2m5MeYcYAGwxRgD0AI8Z4xZCxwAhjf1LQPLRtr+XcBdAKtXr3ZGWmeqHOg7QMQfoW2/PdB0wQXTuXcREXebkmE9x3F+5zhOo+M48x3HmY8dujvfcZzDwAPAHw/M2lsH9DqOc2gq6piIg/0H7TlOr0Tw+3XBVxGR6TShntNJegi4EtgFJID3lqCGEyrenv2VHSHq66G2ttQViYi4x7SE00Dvqfi9A9w6HfudiPZ4O4sbFrNnl18z9UREppmuEDGCeCZOPBsnYuo4cMBQVweVlaWuSkTEPRROIyhOI/f1LqZQMDQ2gkfvlIjItFGTO4LiCbhOx1kAzJpVympERNxH4TSCYs+JThtOa9aUsBgRERdSOI2g2HPKt59BJALLl5e4IBERl1E4jaC1rxWP8RBvn0F1taaRi4hMN4XTCFr7Won4I3QeDhONahq5iMh0UziNoHh1iPYjAaJRqKoqdUUiIu6icBrB4dhhwvkmEnEP0Sj4/aWuSETEXRROIzgSP0IgfgZg7+MkIiLTS+F0jHQuTW+6F29sHgBNTSUuSETEhRROxzgcOwyAt9+Gk6aRi4hMP4XTMYon4Dq9LQCsWFHKakRE3EnhdIy2eBsA2a5ZRCIwY0aJCxIRcSGF0zGK4ZTrnkU0qquRi4iUgsLpGMVwSnbWU10NkUiJCxIRcSGF0zEO9R8i4A3Q3RGmqgoqKkpdkYiI+yicjnE4fphwYQaJmJeaGjCm1BWJiLiPwukYbbG2wRNw6+pKXIyIiEspnI7RlmjDH18AwOzZJS5GRMSlFE7H6Eh0DJ6Au3RpiYsREXEphdMwBadAd7IbeucACicRkVJROA3Tk+oh7+Qp9M6mokIn4IqIlIrCaZjBE3C77Am4mkYuIlIaCqdhiuGU7p5BNKoTcEVESkXhNMzg1SG66tRzEhEpIYXTMO3xdsiEScfDugOuiEgJKZyGaYu3QZ+9VUZtbYmLERFxMYXTMO2Jdvy6PbuISMkpnIbpSHTgj80HYN680tYiIuJmCqdhupJdePrnA7BsWWlrERFxM4XTMN3JbkzfHEKhAnPnlroaERH3UjgN053qptA7myqd4yQiUlIKp2H60n3ke5qJVimcRERKSeE0TG+6l1xvI1VVRuEkIlJCvlIXUC6S2SSZTAHi1VRWKpxEREpJPacB3aluiM8Ax0N1ta4OISJSSgqnAd3JbojNBKCmpsTFiIi4nMJpQHdqKJx0dQgRkdJSOA0Y3nNauLDExYiIuJzCacDwntPZZ5e4GBERl1M4DehJ9UBsJv5gjpkzS12NiIi7KZwGFIf1KioKusmgiEiJKZwGdCW7MPFZVER0B1wRkVJTOA3oTHZCbCaRiEM4XOpqRETcTeE0wIZTk8JJRKQMTGk4GWP+1BjzsjFmmzHmzmHL/8YYs8sYs90Ys2Eqaxirjp4kTrqKykoIBEpdjYiIu03ZtfWMMW8ErgHOdRwnbYxpHFi+FLgeWAbMAh41xpzlOE5+qmoZi452LwDV1QZjSlmJiIhMZc/pFuBzjuOkARzHaRtYfg1wr+M4acdx9gC7gLVTWMeY9HXasbzqaiWTiEipTWU4nQVcbIz5P2PM/xpj1gwsnw3sH7Ze68Cykor3BgGordZhOBGRUpvQsJ4x5lFgpFNWbxvYdh2wDlgD/MAYc8Y4t38zcDPA3Cm8b3q+kCcdsz2nOS3eKduPiIiMzYTCyXGcy473mDHmFuC/HcdxgGeNMQWgATgAzBm2asvAspG2fxdwF8Dq1audidQ6mlgmBsk6AObN07CeiEipTeUY1n3AGwGMMWcBAaADeAC43hgTNMYsABYBz05hHSfUm+4dCKcCU9hBExGRMZrKO+H+O/DvxpitQAZ4z0Avapsx5gfAi0AOuLXUM/V6UzacvME0NTU6yUlEpNSmLJwcx8kA7zrOY58FPjtV+x6vvnTfQDilCIUUTiIipaapaQwN6/mDOYLBUlcjIiIKJ4Z6ToFQQeEkIlIGFE4MHXMKh4zCSUSkDEzlhIhTRm+6F1K1REIeQqFSVyMiIuo5Ab3JfkjWEanw66KvIiJlQOEEHOlKgOMlUqETcEVEyoHCCWjryAEQiZS4EBERARROAHR1269Vleo5iYiUA4UT0N1lQykaVTiJiJQDhRPQ3+MHoHmk66uLiMi0UzgBsT47RW/OHL0dIiLlQK0xkOy3JzfNn6d7OYmIlAOFE5Dpj2D8KZob/aUuRUREUDiRyqUoJGrwBOOEw5oQISJSDlwfTkP3ckrpunoiImXC9eFUvCK5L5hWOImIlAnXh1PxXk6+YFbhJCJSJlwfToP3ctKNBkVEyobrw6knaXtOwZDRFclFRMqE68OpvTcG+RAVIS8+3d1KRKQsuD6cjnRkAIhWqNskIlIuXB9Oxdtl1FRWlLgSEREpcn04dXTZcKqu0pieiEi5cH04dXblAaitKXEhIiIyyPXh1N1jvzbqdhkiImXD9eHU32vfgpbZJS5EREQGuT6cYn32SuTz5rr+rRARKRuub5ET/QFMIE5ttSZEiIiUC9eHUyoWxgTjRMIKJxGRcuH6cMrGI3iCSSordKNBEZFy4epwKjgF8slKvIGkek4iImXE1eHUn+6HZC3eYFoXfRURKSOuDqfedC+kavAFdLsMEZFy4upw6kv3KZxERMqQq8OpO9EL6Sj+QEG3yxARKSOuDqeDHXHAo16TiEiZcXU4HelIAxAJe0tciYiIDOfucOq04VRVoa6TiEg5cXU4Fe/lVKsbDYqIlBVXh1N3twNAU32oxJWIiMhwrg6nnh4DwMwmU+JKRERkOFeHU9/AvZxmNSucRETKiavDKdZnX35zs6vfBhGRsuPqVjkR90EgRk1UU8lFRMqJq8MpGfdiAgkqIwonEZFy4upwSiUCGH+SipCuXSQiUk6mLJyMMSuNMc8YY14wxmwyxqwdWG6MMV82xuwyxvzWGHP+VNVwItlEEBNIEQ4qnEREyslU9pzuBD7tOM5K4PaBnwGuABYN/LsZ+PoU1jCqbCqIx58iHNKwnohIOZnKcHKA6MD31cDBge+vAb7tWM8ANcaY5ims47jyqQo8gQwVQd2iXUSknEzleNZHgIeNMZ/HhuBFA8tnA/uHrdc6sOzQFNYyokK6Ak9VjFBQPScRkXIyoXAyxjwKzBzhoduAS4G/cBznx8aYPwTuBi4b5/Zvxg79MXfu3ImU+hoFp4CTjuD1Z3UvJxGRMjOhZtlxnOOGjTHm28CfD/z4Q+CbA98fAOYMW7VlYNlI278LuAtg9erVzkRqPVYym4RMFV5fjkBgMrcsIiITNZXHnA4Cbxj4/k3AzoHvHwD+eGDW3jqg13GcaR/S60vFbDj583hcPaFeRKT8TOWA1k3APxtjfECKgeE54CHgSmAXkADeO4U1HFdHbwIAnz9fit2LiMgopiycHMf5DbBqhOUOcOtU7Xes2rqTAPgDkzpaKCIik8C1A1qdPfYuuAG/rkguIlJuXBtOXX02nEJBhZOISLlxbTh192UACAc1VU9EpNy4Npx6+7IAVFYES1yJiIgcy7Xh1NOfA6A+qksXiYiUG9eGU3/cTiGvrwmVuBIRETmWa8MpFisA0NxQUeJKRETkWK4Np/6YPb+paYaG9UREyo1rwyket1/r6zSVXESk3Lg3nBIOeDJUR137FoiIlC3XtszJuAf8Kd3LSUSkDLk3nBJejD9JOKCbOYmIlBvXhlM65cX4UwQD6jmJiJQb14ZTNhmw4eRXOImIlBvXhlMuHcD4M4SDGtYTESk3Lg6nIB5fRsN6IiJlyLXhVMiE1HMSESlT7g2ndBiPL0tIs/VERMqOa8PJyVbg9WUJBHSFCBGRcuPKcMoX8pCpwOPL4dUhJxGRsuPKcEpkk5CN4PHnS12KiIiMwJXh1BNLguPF61M4iYiUI1eGU1dfGgCvek4iImXJneHUa8PJ73NKXImIiIzEleHU058BwBdQOImIlCN3hlNfFgC/boIrIlKWXBlOvbEcAMFAiQsREZERuTKc+vrtRIiKsE5yEhEpR64Mp/6BnlO0UuEkIlKO3BlO8QIANdW6rp6ISDlyZzjF7Cy9hhoddBIRKUeuDKd43IZTY32oxJWIiMhIXBlOiYT92qRwEhEpS64Mp2QS8GSZUV1Z6lJERGQE7g0nX5LKsI45iYiUI/eGkz9F0K+p5CIi5ciV4ZRKesCXIuB35csXESl7rmyd0ykPxpchFNB5TiIi5ciV4ZRJecGXJhjQsJ6ISDlyZzilfRhfmoDPlS9fRKTsubJ1zmV8GF9Ww3oiImXKleGUzwTweDMa1hMRKVMuDSc/xpdTz0lEpEy5MpwK2YCG9UREyphLwymIx5fTsJ6ISJlyZTg52RAebw6PK1+9iEj5m1DzbIy5zhizzRhTMMasPuaxvzHG7DLGbDfGbBi2/PKBZbuMMR+fyP5PWi6Ex5cvya5FROTEJtp32ApcCzwxfKExZilwPbAMuBz4F2OM1xjjBb4GXAEsBW4YWHfa5HIO5AN4vIXp3K2IiIzDhGYEOI7zEoAx5tiHrgHudRwnDewxxuwC1g48tstxnFcGnnfvwLovTqSO8eiLZ4EAXr96TiIi5WqqjrrMBvYP+7l1YNnxlk+b7r4UAF6fM527FRGRcThhz8kY8ygwc4SHbnMc5/7JL+mofd8M3Awwd+7cSdlmdywFRPH5NKwnIlKuThhOjuNcdhLbPQDMGfZzy8AyRlk+0r7vAu4CWL169aR0dXr7MwD4dIqTiEjZmqphvQeA640xQWPMAmAR8CywEVhkjFlgjAlgJ008MEU1jKgnlgbA55/OvYqIyHhMqP9gjHkr8BVgBvCgMeYFx3E2OI6zzRjzA+xEhxxwq+M4+YHnfBh4GPAC/+44zrYJvYJx6otlAQiHpnOvIiIyHhOdrfcT4CfHeeyzwGdHWP4Q8NBE9jsR/fEcAJGwzsAVESlXrmuh+xO25xSp0KWLRETKlfvCKWbPb4pWakaEiEi5cl04xRI2nGqimhEhIlKuXBdO8YFwqq8OlrgSERE5HveFU9KefFtXHShxJSIicjyuC6dEwp7LW18dLnElIiJyPO4Lp6T9OqOmorSFiIjIcbkunJIJB7wpaioipS5FRESOw3XhlEga8CcJBjSVXESkXLkunFJJA74Ufq/rXrqIyCnDdS10OmXDKRBw3UsXETlluK6FTqe8GH+aoF+XLxIRKVeuC6ds2ofxZQj4FE4iIuXKneHkzRBQz0lEpGy5LpxyGR/GlyUYUDiJiJQr14VTPhOw4eTXVHIRkXLlynDy+LKaECEiUsZcF06FrO056ZiTiEj5cl04OdkgHm8en07CFREpW65roZ1cEI8vV+oyRERkFK4KJ8cBsmE83nypSxERkVG4KpwyGQccLx6fwklEpJy5Kpy6+1MAeH1OiSsREZHRuDOcvIUSVyIiIqNxVTj19KcB8PnVcxIRKWeuCqfeWAYAn4b1RETKmrvCKV4MpxIXIiIio3JVOPXFsgD4/SUuRERERuWucIrbcAoGTYkrERGR0bgqnPrj9soQkQpdV09EpJy5KpxicXvybWXEVS9bROSU46pWOpaw4VRdqYNOIiLlzFXhFE/Yk29rqxVOIiLlzJXh1FATKHElIiIyGleFUzJpT75tqKkocSUiIjIal4UTQIGGqMJJRKScuSqcEknAnyQaVjiJiJQzV4VTKgn4kgQDun6RiEg5c1k4ecCfxu911csWETnluKqVTqU8GF+KQMBVL1tE5JTjqlY6k/KCL03Ap8sXiYiUM1eFUzbtxfgy+H2uetkiIqccV7XS2bQP48sQDKjnJCJSzlwVTrmMH+PLaFhPRKTMuSqc8pkAxptTOImIlDlXhVMhG8D4spqtJyJS5ibUShtjrjPGbDPGFIwxq4ctX2+M2WyM+d3A1zcNe2zVwPJdxpgvG2Om7ba0hUwQj3pOIiJlb6JdiK3AtcATxyzvAN7iOM45wHuA7wx77OvATcCigX+XT7CGMXOyQYwvR9CvK0SIiJSzCbXSjuO8BHBs58dxnOeH/bgNCBtjgkAdEHUc55mB530b+APg5xOpY8z15kJ4vHnN1hMRKXPTcfDlbcBzjuOkgdlA67DHWgeWTblCARgIJ79X4SQiUs5O2HMyxjwKzBzhodscx7n/BM9dBvwj8OaTKc4YczNwM8DcuXNPZhOD4ok84MXjLbympyciIuXlhOHkOM5lJ7NhY0wL8BPgjx3H2T2w+ADQMmy1loFlx9v3XcBdAKtXr3ZOpo6inlgKiOD1FSayGRERmQZTMqxnjKkBHgQ+7jjOk8XljuMcAvqMMesGZun9MTBq72uydPenARROIiKngIlOJX+rMaYVuBB40Bjz8MBDHwYWArcbY14Y+Nc48NiHgG8Cu4DdTNNkiK6+FAA+/4Q6YCIiMg0mOlvvJ9ihu2OX3wHccZznbAKWT2S/J6Oz14aTZpGLiJQ/11wqoac/A4DPX+JCRETkhNwTTn1ZAAKBEhciIiIn5Jpw6o3ZcKoIaxq5iEi5c0049fXnAKiM6ARcEZFy555wiuUBqKnSuJ6ISLlzTTjF4vb8pvoazYgQESl37gmnmD2/qbEuXOJKRETkRFwTTv1xB0yexurKUpciIiIn4Jpwiscc8MeprgyVuhQRETkB94RTAvAnqQjqmJOISLlzTTilEh7wJwj4XfOSRUROWa5pqVNJH8af1l1wRUROAa65DGom5QNfinCgptSliLhSNpultbWVVCpV6lJkCoVCIVpaWvD7J3YIxTXhlE0FMP6Eek4iJdLa2kpVVRXz58/X3ahPU47j0NnZSWtrKwsWLJjQtlwzrJdNhPEEkgT9CieRUkilUtTX1yuYTmPGGOrr6yeld+yacMqlKnTMSaTEFEynv8n6HbsmnAqpSjz+DOGga0YyRUROWa4IJ8cBUlV4A1n1nERkUv3qV7/iqquumtRt3nfffXzmM58B4IknnuD888/H5/Pxox/96Kj1/uM//oNFixaxaNEi/uM//mNw+WWXXUZ3d/ek1jTdXBFOXb0ZwIPPnycUUM9JxO0cx6FQKJS6DAByudxrlt1555186EMfAmDu3Lncc889vPOd7zxqna6uLj796U/zf//3fzz77LN8+tOfHgykd7/73fzLv/zL1Bc/hVzRUh9ojwF1CieRMvGRX3yEFw6/MKnbXDlzJV+6/EvHfXzv3r1s2LCBCy64gM2bN/PQQw+xfft2PvnJT5JOpznzzDP51re+RWVlJZ/5zGf46U9/SjKZ5KKLLuLf/u3fMMawa9cuPvjBD9Le3o7X6+WHP/whALFYjLe//e1s3bqVVatW8Z//+Z8YY9i8eTMf/ehHicViNDQ0cM8999Dc3Mwll1zCypUr+c1vfsMNN9zAX/7lXw7WuWPHDoLBIA0NDQDMnz8fAI/n6L7Eww8/zPr166mrqwNg/fr1/OIXv+CGG27g6quv5uKLL+a2226bzLd4Wrmi53SwPQaA3+8onERcbOfOnXzoQx9i27ZtRCIR7rjjDh599FGee+45Vq9ezRe+8AUAPvzhD7Nx40a2bt1KMpnkZz/7GQB/9Ed/xK233sqWLVt46qmnaG5uBuD555/nS1/6Ei+++CKvvPIKTz75JNlslj/90z/lRz/6EZs3b+Z973vfUWGRyWTYtGnTUcEE8OSTT3L++eef8LUcOHCAOXPmDP7c0tLCgQMHAKitrSWdTtPZ2TmxN6yEXNFSH+5MAuAPQMDnipcsUtZG6+FMpXnz5rFu3ToAnnnmGV588UVe97rXATYsLrzwQgAef/xx7rzzThKJBF1dXSxbtoxLLrmEAwcO8Na3vhWwJ5sWrV27lpaWFgBWrlzJ3r17qampYevWraxfvx6AfD4/GGYA73jHO0as8dChQ8yYMWPCr7WxsZGDBw9SX18/4W2Vgita6rYuO+c+FHBFR1FEjiMSiQx+7zgO69ev5/vf//5R66RSKT70oQ+xadMm5syZw6c+9akTnrcTDAYHv/d6veRyORzHYdmyZTz99NMnrGW4cDhMb2/vCV/L7Nmz+dWvfjX4c2trK5dccslRryMcPnXvX+eK1rq9Kw1ASDP1RGTAunXrePLJJ9m1axcA8XicHTt2DAZRQ0MDsVhscIZcVVUVLS0t3HfffQCk02kSicRxt7948WLa29sHwymbzbJt27YT1rVkyZLBmkazYcMGHnnkEbq7u+nu7uaRRx5hw4YNgA3ew4cPDx6vOhW5Ipw6u+1smHBI4SQi1owZM7jnnnu44YYbWLFiBRdeeCEvv/wyNTU13HTTTSxfvpwNGzawZs2awed85zvf4ctf/jIrVqzgoosu4vDhw8fdfiAQ4Ec/+hEf+9jHOPfcc1m5ciVPPfXUCet6/etfz/PPP4/j2Lt3b9y4kZaWFn74wx/ygQ98gGXLlgFQV1fH3/3d37FmzRrWrFnD7bffPjg5YvPmzaxbtw7fKXwYwxTfgHK3evVqZ9OmTSf13K8++D98/F+e5II55/M//zq55yOIyNi89NJLLFmypNRlnBL+/M//nLe85S1cdtllJ/38q6++mksvvXSSKxubkX7XxpjNjuOsHus2XNFz+vDvX8qVSy6hMjDyGK+ISDn527/921GHDE9k+fLlJQumyeKKcALI5QtM8AruIiLToqmpiauvvvqkn3/TTTdNYjWl4ZpwAqiI6KKTIiKnAleFU1WFJkSIiJwKXBNOHi9UhBVOIiKnAteEk8+LrkguInKKcE04zVsUZ815oROvKCIyTPE8qC9+8YvcfvvtPProo9New969e1m+fPmkbvP555/nT/7kTwB4+eWXufDCCwkGg3z+858/ar1f/OIXLF68mIULF/K5z31ucPn111/Pzp07J7Wm4U7dM7TG6XN/cqXuwiki43L48GE2btw4pis2jCSfz+P1ln7EJpfLveaE3H/4h3/gE5/4BGBP6P3yl788ePWLonw+z6233sovf/lLWlpaWLNmDVdffTVLly7llltu4c477+Qb3/jGlNTsmnBSMImUj498BF6Y3DtmsHIlfGmU68nu3buXq666iq1btwLw+c9/nlgsxqc+9SkuueQSLrjgAh5//HF6enq4++67ufjii3nzm9/MgQMHWLlyJV/5yle4++67ueqqq1i/fj1r167lgQceYPHixdxwww286U1v4qabbqKyspIPfOADPProo3zta18jHA6PeNuMb3zjG9x1111kMhkWLlzId77zHSoqKjhy5Agf/OAHeeWVVwD4+te/zqxZs8jn89x000089dRTzJ49m/vvv59wOMzu3bu59dZbaW9vp6Kigm984xucffbZ3HjjjYRCIZ5//nle97rXDV5xHaC/v5/f/va3nHvuuYC9SGxjYyMPPvjgUe/Zs88+y8KFCznjjDMA21u6//77Wbp0KRdffDE33njjiME3GVwzrCciMppcLsezzz7Ll770JT796U8D8MADD3DmmWfywgsvcPHFFw+uW11dzVe/+lVuvPFG7r33Xrq7uwfPLYrH41xwwQVs2bKFCy644Li3zbj22mvZuHEjW7ZsYcmSJdx9990A/Nmf/RlveMMb2LJlC88999zg5Yp27tzJrbfeyrZt26ipqeHHP/4xADfffDNf+cpX2Lx5M5///OcHb1II9mKwTz311FHBBLBp06YxDROOdlsOj8fDwoUL2bJly/je6DFyTc9JRMrHaD2cUrn22msBWLVqFXv37j3h+uvXr+eHP/zh4P2dirxeL29729sA2L59+3Fvm7F161Y+8YlP0NPTQywWG7xo62OPPca3v/3twW1VV1fT3d3NggULWLly5VE1xmIxnnrqKa677rrB/afT6cHvr7vuuhGHFSf7thyrVq2a8LaOpXASEVfw+XxH3Zr92NtgFG97UbzlxYkUCgVeeuklKioq6O7uHryfUygUGgyE0W6bceONN3Lfffdx7rnncs899xx1+4uRHHtbjmQySaFQoKamhheOM0Y62m05TnQbELC35di/f//gz62trcyePXvw56m8LYeG9UTEFZqammhra6Ozs5N0Oj14d9uT9cUvfpElS5bwve99j/e+971ks9nXrDPabTP6+/tpbm4mm83y3e9+d/A5l156KV//+tcB29Ma7d5O0WiUBQsWDN4u3nGcMQ2zjfW2HGvWrGHnzp3s2bOHTCbDvffee9RllXbs2DHpswiLFE4i4gp+v5/bb7+dtWvXsn79es4+++yT3tb27dv55je/yT/90z9x8cUX8/rXv5477rjjNeuNdtuMv//7v+eCCy7gda973VG1/PM//zOPP/4455xzDqtWreLFF18ctZbvfve73H333Zx77rksW7aM+++//4T1n3322fT29lTzjU8AAAciSURBVNLf3w/YWYktLS184Qtf4I477qClpYW+vj58Ph9f/epX2bBhA0uWLOEP//APB4+BHTlyhHA4zMyZM8f8vo2HK26ZISKlp1tmlJcvfvGLVFVV8f73v/+knx+NRgfPlRpOt8wQEZGTcssttxx1HGu8ampqeM973jOJFR1NEyJERFwoFArx7ne/+6Sf/973vncSq3kt9ZxEZNqcKocR5ORN1u9Y4SQi0yIUCtHZ2amAOo05jkNnZyeh0MSvY6phPRGZFi0tLbS2ttLe3l7qUmQKhUKhwXO+JkLhJCLTwu/3s2DBglKXIaeICQ3rGWOuM8ZsM8YUjDGvmSJojJlrjIkZY/5q2LLLjTHbjTG7jDEfn8j+RUTk9DTRY05bgWuBJ47z+BeAnxd/MMZ4ga8BVwBLgRuMMUsnWIOIiJxmJjSs5zjOSzDy7SiMMX8A7AHiwxavBXY5jvPKwDr3AtcAo58CLSIirjIlx5yMMZXAx4D1wF8Ne2g2sH/Yz63ABaNs52bg5oEfY8aY7RMoqwHomMDzTyd6L46m92OI3oshei+GTMZ7MW88K58wnIwxjwIjXTzpNsdxjncRp08BX3QcJzaRm/w5jnMXcNdJb2AYY8ym8Vw643Sm9+Joej+G6L34/9s7vxCrijiOf764qSWhaw+ytYIrSOBTmdAuRUSJlkQQ+LAWaP9eiqA/D+HiSz0mESVCCv0hokwzKdkeljKft5JKLV1dM2pF04SUHgLDXw/z271nl3vP4qo7d/f8PjDcOb+Zc34zc773zL1z5sypEW1RI0dbjNs5mdmKCRz3TmCNpE3APOCSpH+B/cDCQr524OQEjh8EQRBMY67JsJ6ZjbwyUtIrwD9mtkVSC7BEUgepU+oGHr0WZQiCIAimLlc6lfwRSUNAF/ClpL6y/Gb2H/Ac0AccBnaa2c9XUobL4KoMD04Toi1GE+1RI9qiRrRFjUlviynzyowgCIKgOsTaekEQBEHTEZ1TEARB0HRUonOaLksmSVooaZ+kX3zZqOfdPl/SV5KO+Wer2yVps9f7gKRlhWOt9/zHJK0v2O+QdND32Sx/FqCRj9xImiHpB0m9vt0hqd/Lv0PSTLfP8u1BT19UOEaP2wckrSrY6+qmkY/cSJonaZekI5IOS+qqqjYkvejfkUOStkuaXRVtSHpP0hlJhwq2bDoo81GKmU3rAMwAjgOLgZnAT8DS3OWaYF3agGUevxE4SloGahOwwe0bgNc8vpq0fJSATqDf7fOBX/2z1eOtnvat55Xv+6Db6/rIHYCXgI+BXt/eCXR7fCvwjMefBbZ6vBvY4fGlrolZQIdrZUaZbhr5yB2AD4CnPT6T9BhH5bRBetj/BHB94Xw9XhVtAPcAy4BDBVs2HTTyMW49cn+hJuFEdQF9he0eoCd3ua5S3b4grcIxALS5rQ0Y8Pg2YG0h/4CnrwW2Fezb3NYGHCnYR/I18pG5/u3AXuA+oNfF/xfQMvbck2aIdnm8xfNprB6G8zXSTZmPzG0xl3RB1hh75bRBbSWa+X6ue4FVVdIGsIjRnVM2HTTyMV4dqjCsV2/JpFsyleWq4UMPtwP9wAIzO+VJp4EFHm9U9zL7UB07JT5y8ibwMnDJt28C/rb0yAKMLv9InT39vOe/3DYq85GTDuAs8L7SMOc7kuZQQW2Y2UngdeB34BTpXO+nutqAvDqY0DW4Cp3TtENp7cLPgBfM7EIxzdJPk2v6fMBk+BgPSQ8BZ8xsf85yNBEtpKGct83sdtKCy6Pur1ZIG62kBaU7gJuBOcADOcvUTEwVHVShczrJNFoySdJ1pI7pIzPb7eY/JbV5ehtwxu2N6l5mb69jL/ORi7uAhyX9BnxCGtp7C5intBIJjC7/SJ09fS5wjstvo3MlPnIyBAyZWb9v7yJ1VlXUxgrghJmdNbOLwG6SXqqqDcirgwldg6vQOX2HL5nkM2e6gT2ZyzQhfFbMu8BhM3ujkLQHGJ5Ns550L2rYvs5ny3QC5/1vdx+wUlKr/8pcSRobPwVckNTpvtaNOVY9H1kwsx4zazezRaRz+o2ZPQbsA9Z4trFtMVz+NZ7f3N7tM7Y6gCWkG751deP7NPKRDTM7Dfwh6VY33U96FU3ltEEazuuUdIOXdbgtKqkNJ6cOGvkoJ8fNuskOpNkiR0kzbDbmLs8V1ONu0l/lA8CPHlaTxrr3AseAr4H5nl+klzseBw4CywvHehIY9PBEwb6c9BLJ48AWaquI1PXRDAG4l9psvcWkC8gg8Ckwy+2zfXvQ0xcX9t/o9R3AZx6V6aaRj9wBuA343vXxOWmWVSW1AbwKHPHyfkiacVcJbQDbSffaLpL+UT+VUwdlPspCLF8UBEEQNB1VGNYLgiAIphjROQVBEARNR3ROQRAEQdMRnVMQBEHQdETnFARBEDQd0TkFQRAETUd0TkEQBEHT8T/alYnNNjcA5QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = pu.plot_results(results, average_group=True, split_fn=lambda _: '', shaded_std=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
